Determination of water treatment parameters based on absorbance and fluorence

ABSTRACT

A computer-implemented method for determining a water treatment parameter includes receiving, by a computer, measurements of a fluorescence emission spectrum of a water sample including a first peak emission wavelength and at least a second peak emission wavelength, emitted in response to an excitation wavelength, receiving, by the computer, an absorbance measurement obtained at the excitation wavelength of the water sample, determining, using the computer, a ratio of the measurements at either the second peak emission wavelength, or a sum of measurements at a plurality of peak emission wavelengths including at least the first peak emission wavelength and the second peak emission wavelength, to the first peak emission wavelength, and calculating, using the computer, a value for the water treatment parameter based on a combination of at least the ratio and the absorbance measurement.

TECHNICAL FIELD

The present disclosure relates to indirect determination of parametersused by water treatment facilities such as biological oxygen demand(BOD), chemical oxygen demand (COD), total organic carbon (TOC),trihalomethane formation potential (THMFP), etc. based on componentsidentified using absorbance and fluorescence spectral analysis.

BACKGROUND

Water treatment plants, including those that treat surface water sourcesmust generally meet various government requirements with respect to boththe effluent distributed from the plant as well as intermediateby-products that may be created during the treatment process. In theUnited States, water treatment plants are required by the EnvironmentalProtection Agency (EPA) to reduce the total organic carbon (TOC)concentration using a process of coagulation prior to disinfection ofthe finished water with halogenated/chlorinated disinfectants.

While various tools or instruments for monitoring TOC have beendeveloped, online monitoring of TOC alone does not provide necessaryinformation on the aromatic properties of the sample that are needed todetermine the effective coagulation and disinfection dosages needed toprevent formation of toxic disinfection by products (DBPs). Thearomaticity is the primary characteristic of the TOC that determines thechemical reactivity with halogenated disinfectants that results intoxic, carcinogenic DBPs.

Current convention uses separate measurements of the absorbance at 254nm (A254 nm) and TOC concentration with separate instruments/detectorsfor the purposes of evaluating the effectiveness of the coagulationusing what is known as the specific UV absorbance calculation, SUVA=A254(m−1)/TOC (mg/l).The TOC and SUVA techniques do not provide areproducible evaluation for different water sources because the aromaticproperties of the organic composition of the source water often variesfor a particular source over time, as well as among different sources.Further, the SUVA parameter is often imprecise due to the lack ofkinetic simultaneity of the parameters of the TOC meter and absorbancedetectors, as well as inherent propagated noise/interferences of theseparate detection methods as conventionally implemented. Both TOC andA254 are prone to interferences of several types. Use of independentfluorescence data provides a means of ameliorating the influence ofprimary interferences.

While previous regulations for water treatment plants in the UnitedStates required averaged readings of disinfection byproduct formationlevels of an entire distribution system, more recent EPA regulationsrequire monitoring of local averages of disinfection byproduct formationlevels in different regions of the distribution system. Monitoring onlysystem wide averages may not be sufficient to detect local regions withhigher propensity to form DBPs, which may be in violation of the morerecent regulations outlined in the EPA Disinfection Byproduct Rule 2(DBPR2). This of course heightens the need for more precise and accurateTOC and aromaticity evaluations for local regions of the treatmentprocesses.

The imprecision of A254, TOC and SUVA as calculated using EPA specifiedmethods (such as EPA Method 415.3) may also be attributed to the factthat both detector readings are aggregate, single point readings andtherefore lack qualitative information on the effects of the coagulationtreatment with respect to reactive organic species. As previouslydescribed, a number of interferences or confounding factors must beconsidered with respect to their effect on the readings, includinginorganic carbon, metals like iron, and unknown contaminants that may ormay not fluoresce. In addition, online TOC meters are highly prone tofalling out of calibration, as are online DBP meters (gaschromatographs). Most water treatment plants cannot afford to installand maintain these pieces of equipment. However, as a result of therecent regulatory requirements, many water treatment plants in theUnited States are considering major infrastructure changes (tens ofmillions of dollars) including addition of ozone-destruction and ionexchange processes, such as the MIEX resin treatment process, forexample.

SUMMARY

A computer-implemented method for determining a water treatmentparameter includes receiving, by a computer, measurements of afluorescence emission spectrum of a water sample including a first peakemission wavelength and at least a second peak emission wavelength,emitted in response to an excitation wavelength, receiving, by thecomputer, an absorbance measurement obtained at the excitationwavelength of the water sample, determining, using the computer, a ratioof the measurements at either the second peak emission wavelength, or asum of measurements at a plurality of peak emission wavelengthsincluding at least the first peak emission wavelength and the secondpeak emission wavelength, to the first peak emission wavelength, andcalculating, using the computer, a value for the water treatmentparameter based on a linear combination of at least the ratio and theabsorbance measurement. The method may provide indirect determination ofparameters used by water treatment facilities such as biochemical oxygendemand (BOD), chemical oxygen demand (COD), total organic carbon (TOC),Trihalomethane and Haloacetic Acid formation potentials (HAAFP andTHMFP, respectively), etc. based on a ratio of components identifiedusing absorbance and fluorescence spectral analysis rather than relyingon instruments, such as a TOC meter, that are subject to moreinterferences and related variance in readings.

In one embodiment, a computer-implemented method for controlling a watertreatment process includes measuring a first fluorescence emissionspectrum of a pre-process water sample over a predetermined wavelengthrange produced in response to an excitation wavelength, normalizing thefirst fluorescence emission spectrum to a predetermined peak value,measuring a second fluorescence emission spectrum of a post-processwater sample over the predetermined wavelength range produced inresponse to the excitation wavelength, normalizing the secondfluorescence emission spectrum to the predetermined peak value,comparing the first and second peak normalized fluorescence emissionspectra to determine a change in dissolved organic carbon (DOC), andcontrolling the water treatment process based on the change in DOC.

Embodiments may also include a system for monitoring a water treatmentprocess that incorporates a coagulation-settling or flocculation-onlyprocess. The system may include a first instrument positioned for onlinesampling of an inlet to the coagulation-settling process, the firstinstrument measuring a first fluorescence emission spectrum of an inletsample in response to a first excitation wavelength, a second instrumentpositioned for online sampling of an outlet from thecoagulation-settling process, the second instrument measuring a secondfluorescence emission spectrum of an outlet sample in response to thefirst excitation wavelength, and a computer in communication with thefirst and second instruments and configured to compare the firstfluorescence emission spectrum and the second fluorescence emissionspectrum for controlling the coagulation-settling (or flocculation)process.

Systems and methods according to various embodiments of the presentdisclosure may provide a number of advantages. For example, embodimentsmay be used to provide a more rapid, precise and accurate indirectdetermination of one or more water treatment parameters used formonitoring or control of water treatment facilities such as biologicaloxygen demand (BOD), chemical oxygen demand (COD), total organic carbon(TOC), and trihalomethane formation potential (THMFP) using onlineabsorbance corrected fluorescence excitation-emission spectroscopy.

Various embodiments provide simultaneous determination of the TOC,aromaticity and SUVA parameters using one or more online instrumentspositioned at one or more key points of a water treatment system. Asynchronized calibrated network of online instruments provides fullultraviolet-visible (UV-VIS) absorbance and corrected fluorescenceemission spectra on the order of milliseconds to seconds for real-timeor near real-time monitoring and control of one or more treatmentprocesses. Embodiments of the system and method of this disclosurefacilitate cost-effective improvement of current water treatmentinfrastructure methodology (such as using enhanced coagulation oradditional granular activated carbon) to ameliorate predicteddisinfectant byproduct (DPB) spikes as well as to avoid overdosing ofcoagulant when DBP potential is low.

The online absorbance and fluorescence spectral analyses according toembodiments of this disclosure may be used to identify or flag unknowncontaminations as new components. The system or method for indirectdetermination of a water treatment parameter according to embodiments ofthis disclosure include a model that uses the spectral analyses todetermine various application or process specific treatment parametersto monitor and/or control the treatment process. The system and methodof various embodiments may be used to detect membrane fouling agents fora variety of membrane systems including: reverse osmosis,microfiltration, ultrafiltration and membrane bioreactors and forwardosmosis (ceramic membranes). Fouling agents may have associated spectralpeaks. As such, the system and method may be used for a variety oftreatment plants including desalination, wastewater recycling andindustrial treatment as well as for shipboard ballast purificationsystems using membrane technology. The model may be tuned for variousapplications including surface water treatment, wastewater treatment, orindustrial treatment processes. In various embodiments, the model istuned to protein like peaks for sewage treatment to provide an indirectdetermination of biologic oxygen demand (BOD) or chemical oxygen demand(COD). Similarly, the model may be tuned to oil peaks for an oilrecycling application, etc. Oxygen concentration is another factor thatcan be quantified in the model as an influencer of the quantum yield ofthe component species for ozone treatment monitoring by evaluating theabsorbance and fluorescence data. Oxygen in aqueous solution, associatedwith ozone treatment, lowers the fluorescent quantum yield (bycollisional quenching) of a given chemical species but normally does notlower its absorbance extinction coefficient Hence by evaluating thechange in fluorescence intensity and absorbance one can evaluate theoxygen concentration and changes in the concentration as a function ofthe treatment process.

Other applications and advantages of a system or method according toembodiments of the present disclosure include use to effectively monitorand accurately determine the replacement period for bio-active carbon(BAC) filters by analyzing the levels or ratios of C1-C4 components. TheBAC activity primarily influences the protein-like peaks following ozonetreatment. This can potentially save millions of pounds of activatedcarbon and millions of dollar a year. One water treatment facility hasestimated that it can extend the useful life of their BAC filter matsfor up to several years at huge cost savings with an accuratemeasurement of effectiveness rather than following the recommendationfor annual replacements by the carbon mat suppliers.

Using instruments with one or more multichannel detector(s), collectionand processing of the absorbance and fluorescence data is effectivelyinstantaneous (within seconds) relative to many offline analysisstrategies. Based on the representative embodiments disclosed,algorithms can be easily calibrated and validated to precisely andaccurately quantify the compounds removed specifically by coagulation,ozone, or other processes.

The online absorbance and fluorescence measurements may be combined withother online monitor quality metrics including nephelometric turbidityunits (NTU), chlorine dose and residual, pH, alkalinity (hardness), andtemperature, for example, which all can easily be incorporated into acontinuously updated, calibrated predictive model for highly effectivedeterminations of disinfection byproduct formation potential and TOC.

The above advantages and other advantages and features of the presentdisclosure will be readily apparent from the following detaileddescription of the preferred embodiments when taken in connection withthe accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a simplified block diagram illustrating a representative watertreatment facility for treating surface water and positioning ofinstruments for monitoring and/or controlling one or more treatmentprocesses using water treatment parameters determined according to thepresent disclosure;

FIG. 2 is a simplified block diagram of an instrument that may be usedto provide absorbance measurements and absorbance corrected fluorescenceexcitation-emission spectra measurements for use in indirectdetermination of water treatment parameters according to embodiments ofthe present disclosure;

FIGS. 3A and 3B are simplified flow charts illustrating calibration andoperation of a system or method for indirect determination of watertreatment parameters according to embodiments of the present disclosure;

FIGS. 4-8 illustrate examples of determining component concentrations C1and C2 or similar components for use in indirect determination of awater treatment parameter according to embodiments of the presentdisclosure; and

FIG. 9 is a flow chart illustrating operation of a system or method fordetermining a water treatment process parameter or indicator, such asdissolved organic carbon (DOC), total organic carbon (TOC), ortrihalomethane formation potential (THMFP), for example, usingabsorbance and fluorescence measurements according to embodiments of thepresent disclosure.

DETAILED DESCRIPTION

Various representative embodiments of systems and methods according tothe present disclosure are described in detail. However, it is to beunderstood that the representative embodiments are merely exemplary andsystems and methods according to the present disclosure may be embodiedin various and alternative forms. The figures are not necessarily toscale and some features may be exaggerated or minimized to show detailsof particular components. Therefore, specific structural and functionaldetails disclosed herein are not to be interpreted as limiting, butmerely as a representative basis for teaching one of ordinary skill inthe art to variously employ the present invention. Simplified flowchartsor block diagrams may be provided to illustrate operation of arepresentative embodiment of a system or method. Those of ordinary skillin the art will appreciate that the order of steps or processes may notbe required for particular applications, that some steps may have beenomitted for ease of illustration and description, and that steps orprocesses may be repeated individually and/or as a subset of theillustrated steps or processes. Likewise, all illustrated or describedsteps may not be needed to provide one or more the advantages describedherein.

As those of ordinary skill in the art will also understand, variousfeatures of the present disclosure as illustrated and described withreference to any one of the Figures may be combined with featuresillustrated in one or more other Figures to produce embodiments of thepresent disclosure that are not explicitly illustrated or described. Thecombinations of features illustrated provide representative embodimentsfor typical applications. However, various combinations andmodifications of the features consistent with the teachings of thepresent disclosure may be desired for particular applications orimplementations.

A simplified block diagram illustrating a representative water treatmentfacility having various water treatment processes is shown in FIG. 1.While various embodiments are illustrated and described with respect toa representative water treatment process used for surface watertreatment, various embodiments may include other types of watertreatment facilities and/or processes. As described in greater detailbelow, concepts described in the present disclosure may be applied to awide variety of treatment processes to monitor and/or control theprocess. Online monitoring using absorbance and fluorescencemeasurements according to embodiments of the present disclosure may beused to improve the precision and accuracy of a number of parametersused to monitor or control various water treatment processes, includingbut not limited to BOD, COD, DOC, TOC, THMFP and various otherparameters.

While a number of variations exist, most surface water treatmentfacilities have a number of key processes that include dilution,coagulation/flocculation, settling, filtration, and disinfection.Various other chemical treatments or processes may also be useddepending on the type of source water and expected contaminants. Thecomplexity and type of treatments or processes may depend on the qualityof the source water and variation over time based on the effectivenessof source-water protection and management programs.

As illustrated in FIG. 1, a representative surface water treatmentfacility 100 may receive surface water from one or more sources, such asa river, lake, reservoir, etc. as generally represented at 102. Watertreatment facility 100 may include one or more holding areas, tanks, orreservoirs 104 to provide dilution or mixing of sources. An opticalinstrument 110 may be positioned online at the inlet from one or moresources, and/or after combination of source water. Online instrument 110facilitates real-time monitoring of one or more water treatmentparameters that may be directly or indirectly determined based onabsorbance and fluorescence measurements for use in monitoring orcontrolling subsequent treatment processes. Online instrument 110 maycommunicate over a wired or wireless network 116 with a local monitoringor control computer/server 120 as well as one or more remotecomputers/servers 150 that may communicate over a wide area network(WAN) such as the internet or cloud. In addition, online instrument 110may be optically coupled via one or more optical fibers or cables 112 toa calibration source 114 for in-situ calibration as described in greaterdetail below. Similarly, online instrument 110 may communicate directlyor indirectly over a wired or wireless network with other similarinstruments 122, 124, 126 that provide absorbance and fluorescencemeasurements, as well as sensors or meters such as pH sensor 130, TOCmeter 132, turbidity detector 134, temperature sensor 136, and hardnesssensor 138. Those of ordinary skill in the art will recognize that thetypes, numbers, and locations of instruments, sensors, detectors,meters, etc. may vary by particular application and implementation.

Depending on the particular application and implementation, a single (orcommon) instrument may be used to measure or monitor a water treatmentparameter before and after a particular treatment process rather thanhaving separate instruments, as represented by optical instruments 110,and 126. Plumbing 106, 108 may be provided to route online samples fromupstream and downstream of one or more treatment processes to asingle/common instrument. While this may provide cost savings byreducing the number of instruments needed for monitoring of the desiredtreatment processes, the cost savings may be reduced by additional watersample routing and control to a common location, as well the additionalmanual or automatic hardware required to change the sample flow throughthe instrument. Instruments or monitors may be positioned at keylocations relating to essential steps in the treatment plant aspreviously described. These usually include at least the inlet, aftercoagulation-settling, and at the plant effluent. Online monitorspositioned at these locations, or one or more central or common monitorsthat receive online samples from these locations, may be used to samplethe water flow at regular time intervals. Water treatment parameters andassociated measurement or determination protocols may be developed forparticular types of treatment facilities. Representative water treatmentparameters and associated representative measurement methods orprotocols according to embodiments of the present disclosure aredescribed below.

Water treatment facility 100 may include a flocculation and/orcoagulation process as generally represented at 140 a and 140 b,respectively. As known, coagulation/flocculation may include additionand mixing of chemical coagulants or filter aids that cause smallsuspended particles to stick together to form larger particles that morereadily settle out or can be more easily filtered. The type and quantityof coagulants may depend on the microbial and chemical components of thewater. This process in combination with a settling process may be usedto remove organic carbon materials from the water to reduce formation ofundesirable disinfection byproducts during the subsequent disinfectiontreatment/process 160.The coagulation process may be monitored and/orcontrolled by comparing measurements from instrument 124 positioneddownstream or subsequent to coagulation process 140 with one or moreupstream instruments, such as instruments 110, 122 as described ingreater detail herein.

A settling or sedimentation process (not shown) may followcoagulation/flocculation process 140 a-b, particularly in applicationstreating water that contains significant solids. The settling orsedimentation process slows the flow of the water in a basin or pond toallow heavier items to settle to the bottom rather than being carried inthe flow to the next process. Online instrument 124 may be positioneddownstream of such a sedimentation process with measurements used todetermine one or more water treatment parameters used to monitor orcontrol the process by comparing measurements with an upstreaminstrument, such as instrument 122 or 110, for example. A settling orpre-sedimentation process (not shown) may be used prior to the dilutionprocess 104 in some applications.

After settling or sedimentation, a single or multi-step filtrationprocess 150 may employ a variety of methods to filter particles from thewater. The type of filtration may vary based on the raw water quality.As filtration implies, water flows through a material that captures andremoves particles, organisms, and/or contaminants. Granular media suchas sand, crushed anthracite coal, or granular activated carbon (GAC) areoften used as filter media. Different types and sizes of media may belayered and may operate at different flow rates. Membrane filtration mayalso be used in some applications, but may not be well-suited for highlycontaminated source waters because of membrane clogging. Membranefiltration is gaining use in the United States for special applicationsand in combination with other types of filtration. Online instrument 126may be positioned downstream of filtration process 150 with associatedmeasurements used to determine water treatment parameters for monitoringor controlling the process. In the representative embodimentillustrated, online optical instrument 126 may receive water samplesafter filtration process 150, as well as samples after disinfectionprocess 160.

While filtration and the steps prior to filtration focus on the physicalremoval of contaminants in the water, disinfection is used to kill orinactivate bacteria and viruses that may pass through the physicalremoval steps. Viruses and organisms like giardia are effectively killedby chlorine. Some organisms, such as Cryptosporidium may be resistant tochlorine, but are susceptible to treatment by ozone and ultravioletlight. In some countries, ozone and UV light may be used withoutchlorination to kill bacteria and other organisms. In the representativeapplication for an optical instrument and associated monitoring andcontrol of a water treatment facility, disinfection process 160 provideschemical disinfection, but may also generate undesirable disinfectionbyproducts when reacting with organic carbon components. Disinfectantsmay include chlorine, chloramines (chlorine plus ammonia), ozone,ultraviolet light, and chlorine dioxide, for example. The advantage ofchlorination is that it continues to kill bacteria as water movesthrough the distribution system 170. Its disadvantage is the possibilityof potentially harmful disinfection byproducts. Excess chlorine in watercan combine with organic material in the water to form substances suchas trihalomethanes, which have been linked to various adverse humanhealth effects over a lifetime exposure. Use of one or more onlineinstruments to monitor and/or control one or more water treatmentprocesses or parameters according to embodiments of the presentdisclosure may reduce disinfection byproducts by more accuratemonitoring and control of one or more upstream processes, such ascoagulation process 140 b, in addition to more accurate monitoring andcontrol of the disinfection process 160.

Various other treatment processes, may be used to treat or condition thewater at various points of the overall treatment process. For example,chemicals may be added to drinking water to adjust its hardness(softness), pH, and alkalinity to reduce corrosion in the distributionsystem 170, which may include pipelines, storage tanks, and buildingplumbing systems, for example. Similarly, fluoride may be added to thewater to enhance dental health.

FIG. 2 is a simplified block diagram of an instrument that may be usedto provide absorbance measurements and absorbance corrected fluorescenceexcitation-emission spectra measurements for use in indirectdetermination of water treatment parameters according to embodiments ofthe present disclosure. The instrument and various alternatives aredescribed in greater detail in commonly owned and copending U.S. patentapplication Ser. No. ______, titled ______, (Attorney Docket No.HJY0198PUS) the disclosure of which is hereby incorporated by referencein its entirety. Other instruments may be used to provide similarabsorbance and absorbance corrected fluorescence measurements accordingto the present disclosure, such as those disclosed in copending andcommonly owned U.S. patent application Ser. No. 13/042,920, thedisclosure of which is hereby incorporated by reference in its entirety.A commercially available implementation of such an instrument is theAQUALOG™ available in the United States from Horiba Scientific ofEdison, N.J.

As generally illustrated in FIG. 2, a representative instrument, such asinstrument 110 includes an excitation light source 202 that may includea narrow or broadband light source 204 used in combination with one ormore filters, such as white light filter 206 and excitation filter 208.Alternatively, a white light filter 206 may be replaced with a whitelight diode used in combination with excitation filter 208. In otherembodiments, an excitation diode emitting light of the desiredexcitation wavelength(s) may be used in place of a light source incombination with an excitation filter. As described in greater detailbelow, excitation filter 208 (or an excitation diode or source) isselected to provide narrowband light having a wavelength of 254 nm witha bandwidth of about 2 nm. Light from source 202 passes through abeamsplitter 210 with a portion reflected to a reference detector 212 toprovide light and temperature compensation. Use of a reference diode ordetector 212 for excitation monitoring and correction provides traceableoptical correction of fluorescence excitation spectra and compensatesfor any input lamp drift. Light transmitted through beamsplitter 210passes through a flow-through cuvette 214 having a fixed path length andpositioned using quartz rods/guides (not shown). Water from one or moretreatment processes or steps as described above with reference to FIG. 1is routed to flow-through cuvette 214 for analysis.

Light passing through cuvette 214 is coupled to fiber link 220 androuted to multi-channel detector 250, implemented by a CCD device invarious embodiments. Fiber link 220 includes a linear array of fibers toroute the light transmitted through cuvette 214 to grating 232 and aseparate portion of detector 250 so that a single CCD or othermultichannel detector 250 may be used for both absorbance andfluorescence measurements to reduce instrument cost. This isparticularly beneficial in reducing system costs associated withincorporating a network of instruments/monitors to monitor or controlmultiple steps or processes within a water treatment facility accordingto embodiments of the present disclosure. In addition, use of a fibercoupling or link 220 facilitates insertion of light injection from asolid state reference material and/or a calibrated light source 114 forwavelength and spectral accuracy in calibration of multichannel detector250. This facilitates in-situ calibration of the instrument as describedin greater detail herein.

Light generated by fluorescence of the sample within flow-throughcuvette 214 passes through an emission filter 230 to grating 232, whichseparates the light into wavelength bands before passing through ordersorting emission filter 246 to multi-channel detector 250. Similarly, aspreviously described, light passing through cuvette 214 travels throughfiber coupling link 220 and is routed to grating 232 and to detector 250for obtaining absorbance measurements. Instrument 110 includes acomputer 252 to receive the absorbance and fluorescence measurements andcorrect the fluorescence spectrum using the absorbance measurements. Themeasurements may be communicated over a local or wide area network 116to a separate computer/server 120, 150 for use in determining variouswater treatment parameters and for monitoring and controlling the watertreatment process. Alternatively, the computer associated with anyparticular instrument may be used to determine the water treatmentparameters and communicate them to a central monitoring/controlcomputer, server, or system. The computer associated with eachinstrument may also perform various instrument control functions such ascontrol of sample water routed to cuvette 214, control of one or moreshutters (not specifically illustrated) positioned to block light fromlight source 202, or in the absorbance or fluorescence optical paths foruse during calibration etc. The computer may also correct measurementsusing the reference detector 212.

Other embodiments of the instrument could include use of separatemultichannel detector-spectrographs to measure fluorescence andabsorbance. However, use of an instrument having a single multichanneldetector-spectrograph to simultaneously measure fluorescence andabsorbance may provide various advantages. The main advantage of such aninstrument is the full spectral correction and coordinated wavelengthcalibration afforded by the multichannel detector for both absorbanceand fluorescence along with streamlined rapid data collection andprocessing.

FIGS. 3A and 3B are simplified flow charts illustrating calibration andoperation of a system or method for indirect determination of watertreatment parameters according to embodiments of the present disclosure.As represented by the flow charts in FIGS. 3A and 3B, a water treatmentparameter such as total organic carbon (TOC) or dissolved organic carbon(DOC) is determined or evaluated from absorbance and fluorescencespectral data using one or more online detectors 110, 122, 124, 126alone or in combination with sensors and meters such as one or more pHsensors 130, TOC meters 132, turbidity detectors 134, temperaturesensors 136, and hardness sensors 138 that communicate within a detectornetwork. Each online optical detector 110, 122, 124, and 126 may besynchronized with the other online optical detectors. TOC (or DOC) meterreadings and other parameters are correlated with treatment steps orprocesses periodically as per regulations to verify online sensors andoptical instruments.

Optical instruments/detectors 110, 122, 124, 126 are positioned atselect treatment steps to monitor complete absorbance spectrum andinner-filter effect corrected fluorescence emission spectra at selectedexcitation wavelengths including but not limited to 254 nm (A254) fortotal and aromatic carbon concentration, key chlorophyll wavelengths,key petroleum or contaminant wavelengths, protein peaks for BOD/COD etc.Regular recording of DOC can be used to calibrate optical online metersas a function of treatment steps and account for quality differenceswithin and between treatment facilities or plants.

In the representative embodiments of FIGS. 3A and 3B, the absorbance andfluorescence measurements obtained from the optical instruments 110,122, 124, and 126 are used to determine water treatment parameters, suchas dissolved organic carbon (DOC) or total organic carbon (TOC), forexample, as described below.

The absorbance at 254 nm (represented by A254 nm) corresponds to:

A254 nm=ε_(254 nm)·DOC·l, where ε_(254 nm) is the molar extinctioncoefficient, DOC is the dissolved organic carbon concentration usuallyexpressed in (mg/l) and l is the absorbance path length. DOC generallycomprises a sum of 3 to 4 well defined spectral components (C1-C4) orspecies that are of natural origin with different fluorescence emissionswhen excited at 254 nm including:

-   -   C1=High molecular weight humic/fulvic acids (excitation (ex) 254        nm, emission (em) 475 nm);    -   C2=Mid to low molecular weight (MW) humic and fulvic acids (ex        254 nm, em 425 nm);    -   C3=protein like components (ex254 nm, em 350 nm); and    -   C4=low mw and protein like components (ex254 nm, em 325 nm)        Virtually all naturally sourced surface water can be modeled as        a sum of components C1 to C3 accounting for >99% of spectral        variance. It follows then that the fluorescence spectrum excited        at 254 nm contains >99% of the information needed to evaluate        changes in any of the 3 or 4 components as a function of a        particular water treatment process or sequence of processes.

Various process parameters such as C1, C2, A254, pH, temperature, etc.may be selected to indirectly determine the desired monitoringparameter, such as DOC, TOC, THMFP, etc. Selection of suitableparameters may include identifying the parameters such as C1, C2 pHtemperature, etc. having some relationship or correlation with themonitoring parameter(s). Once these parameters are identified, a linearcombination of the parameters may be determined. For example, a linearcombination such as xA254+yC1/C2+zpH+ . . . . The relationship betweenmeasured values and the linear product is used to determine the slopeand x, y, and z scaling factors using any of a number of curve fittingstrategies, such as multiple linear regression (MLR), for example. Thisprocess is repeated over time with measurements from representativesamples for the particular application to fine tune the slope, scalingfactor(s), and/or base parameters used in the linear combination toachieve a desired correlation between the base parameter and themonitoring parameter.

In one embodiment for monitoring a water treatment process, TOC wasselected as the monitoring parameter. To select the base parameters anddetermine the slope and scaling factors for the linear combination usedto indirectly determine TOC, the component concentrations or values forC1 and C2 were selected that correlate with the SUVA value because the.SUVA correlates with the aromatic DOC component. The A254/DOC valuescorrelate with the aromatic component concentration of C1 and C2. The C1component is the humic component with the highest aromaticity andmolecular weight (MW) and the strongest correlation with high SUVAaromaticity index. The C2 component is the fulvic/humic component withlower MW and aromaticity and hence correlates with a low SUVAaromaticity index. C3 and other components with deeper UV absorbance andemission than C1 and C2 are generally less influenced by, and thereforenot well correlated with coagulation. They are much lower in aromaticityand tend to vary independently of coagulation as compared to C1 and C2so they are not effective predictors of aromaticity. As such, C3 and C4were not selected because including these components in an MLRprediction of TOC or DOC would result in poor correlation and predictivecapability of the selected process monitoring parameter.

The integral of the DOC fluorescence spectrum measured from 290-600 nmin response to excitation at 254 nm is represented by:

${\int\limits_{290\mspace{14mu} {nm}}^{600\mspace{14mu} {nm}}F_{\lambda}} = {{\int\limits_{290\mspace{14mu} {nm}}^{600\mspace{14mu} {nm}}{C\; 1}} + {\int\limits_{290\mspace{14mu} {nm}}^{600\mspace{14mu} {nm}}{C\; 2}} + {\int\limits_{290\mspace{14mu} {nm}}^{600\mspace{14mu} {nm}}{C\; 3}} + {\int\limits_{290\mspace{14mu} {nm}}^{600\mspace{14mu} {nm}}{C\; 4}}}$

The quantum contribution of each component (C1-C4) excited at 254 nm isdefined in general as:

ΦC _(x) =A254C _(x) ·T·pH

Where temperature (T) and (pH) may influence quantum contributions butgenerally the contributions statistically are negligibly different amongthe four components and pH also plays a minor role across treatmentprocess for the four components, meaning temperature and pH are normallynot large factors in typical surface water treatment applications, butare accounted for in the model to provide broader applicability.

Water treatment directly alters the apparent molar extinctioncoefficient (ε_(254 nm)) primarily by changing the relativeconcentrations of the components(Cx), as opposed to changing theirquantum yields or generating new fluorescent components. As such,evaluating the (normalized) spectral profile of the fluorescencespectrum excited at 254 nm provides a direct measurement of the relativeconcentrations of the components C1-C4. The normalized spectral profilescaptured from samples taken before and after a particular treatmentprocess or series of treatment processes may be used to monitor and/orcontrol the process(es) as described in this disclosure. Evaluation ofthe spectral profiles may be performed using various techniques asgenerally represented and described with reference to FIGS. 5-8.

The largest influence of coagulation (as a representative treatmentexample) is to reduce the apparent molar extinctioncoefficient(ε_(254 nm)) by reducing the concentration of C1 relative tothe total or sum of components of C_(tot)=(C1+C2+C3), which provides adirect measurement of the aromatic concentration. The ratio ofC1:C_(tot) is linearly related to, and the primary cause of the changein slope of A254/DOC, which is also referred to as the specific UVabsorbance (SUVA). In many applications, various treatment processes maynot affect all of the components C1-C4 equally. As such, the particularratio used to monitor or control a particular process may vary. Forexample, the concentrations of C3 and C4 are not significantly changedby a coagulation treatment process. As such, the process can bemonitored, evaluated, and/or controlled by determining correspondingtreatment parameters, such as DOC, using a ratio of C1:C2 rather thanC1:C_(tot).

As one can see from the above description, C1:C_(tot)=xSUVA where x is asimple linear scaling factor such that the relationship remains linearfor:

DOC=A254 nm/ε_(254 nm) ·l

because changes in the molar extinction coefficient(ε_(254 nm)) arelinearly related to the sum of the components represented by C_(tot) inthis example.

As previously described, C4 is not normally detected as a naturalcomponent in surface water treatment. However, it may be monitored fordetection of particular types of pollutants and is highly prevalent insewage sourced wastewater and water sources receiving treated sewage.The changes in components represented by C1-C4 can be monitoredcontinuously and evaluated by comparing spectra from the differentdetectors 110, 122, 124, 126, etc. in the network within a particulartreatment facility 100, or among treatment facilities. Representativecomparisons are illustrated and described with respect to FIGS. 5-8.

The optical signal processing of the present disclosure is designed tomonitor two main sources of relative and absolute component variation ofthe DOC. For natural and manmade raw source variation, the relative DOCcomponent contribution varies depending on several factors of the sourcewater including rain and storm events, fire events, leaf fall events,algal blooms, sewage discharge, etc. For treatment process variation,the primary effect of the coagulation and filtration processes is toinfluence the total DOC as well as the relative aromatic carboncomponent ratios. The main function of the treatments is to reduce theprobability of the formation of regulated and undesirable halogenateddisinfection byproducts by reducing the precursor components containedwithin the DOC.

The flowcharts of FIGS. 3A and 3B provide a representative experiment ormeasurement protocol or method including instrument calibration that maybe used at a monitoring or control point for a particular watertreatment process. The water treatment parameters indirectly determined,calculated, or measured may vary depending on the particular treatmentprocess being monitored or controlled.

FIG. 3A is a flow chart illustrating a representative calibrationprocess for use in indirect determination of water treatment parametersaccording to embodiments of the present disclosure. A calibrated DOCmeter is used to measure DOC as represented at 310. A commerciallyavailable DOC meter may be used with the DOC meter calibration procedurespecified by the manufacturer and/or a calibration process approved by aregulatory agency or standards organization, for example. An instrumentsuch as described and illustrated with reference to FIG. 2 is then usedto determine A254 and SUVA parameters as represented by block 312. Invarious embodiments, A254 and SUVA parameters are determined accordingto U.S. EPA Method 415.3 for an initial calibration period (e.g. 30-90days) of normal water treatment (or other facility)plant operation forraw, settled, and effluent sample sets.

With continuing reference to FIG. 3A, the instrument is used to measureor obtain an absorbance corrected fluorescence spectrum (ranging from290 nm to at least about 600 nm, for example) in response to excitationat 254 nm as represented at 314. Alternatively, a complete fluorescenceexcitation emission spectrum (FEEM) may be obtained for eachcorresponding DOC and A254 reading from block 310. Constituents orcomponents within the samples are then quantified using one or morequantitative analysis tools as represented by block 316. Quantitativeanalysis tools may include principal component analysis (PCA), parallelfactor analysis (PARAFAC, which is a generalization of PCA), classicallinear regression (CLR), peak value analysis, etc. to identify orquantify each of a plurality of components within the samples. This stepmay include quantification of various components associated with theparticular process being monitored or controlled. Various embodimentsaccording to the present disclosure quantify components C1-C4 aspreviously described for use in monitoring and/or control of a watertreatment process.

Alternatively, or in combination, an absolute concentration ratio ofcomponents may be determined as represented by block 318. In oneembodiment, an absolute concentration ratio of C1:C2 is determined alongwith the corresponding A254 measurement for use as linear coefficients(x and y) with the intercept fixed at 0 mg/l to predict DOC. Accordingto embodiments of the present disclosure, predicted DOC may berepresented by a generally linear function (x[C1]:[C2]+yA254) asdescribed in greater detail with reference to FIGS. 4-8.

A multiple linear regression (MLR) is used to determine the x and ycoefficients and corresponding slope as represented by block 320 and asdescribed in greater detail with reference to FIG. 4. The predictive MLRmodel determined at block 320 is evaluated to determine a correlationcoefficient using analysis of variance (ANOVA) to determine a confidenceinterval for the slope as represented at 322.

The calibration may be periodically checked as generally represented byblock 324 although this is not part of the calibration process per se,but is provided for purposes of illustration of a representative watertreatment process. In particular, the predictive MLR model may beapplied in continuous sampling mode with periodic re-evaluation comparedto daily batch analyses, for example. The C1:C2 ratio may also bedetermined as a surrogate of the Aromatic Index and compared to SUVA.

As illustrated in the flowchart of FIG. 3B, in one embodiment, thefluorescence emission spectrum (250 nm to 600 nm, for example) ismeasured with a CCD-spectrograph, such as instrument 110, in response toan excitation filtered to 254 nm. The instrument may be calibrated witha blank with data stored in a non-transitory computer readable storagemedium for future use as represented by l_(o) at block 340. Anabsorbance measurement is performed with the white light (l) source(from 250 nm to 800 nm, for example) using an order sorted photodiodearray spectrograph as represented at 342. The absorbance spectrum iscalculated for 250 nm to 600 nm as −log(l/lo) as represented at 344. Theinstrument is then used to determine the fluorescence spectrum of asample cell as represented at 346. In one embodiment, the sample flowthrough the flow-through cuvette is stopped during the absorbance andfluorescence measurements. However, in other embodiments the samplewater may continue to flow during the measurements, although this mayintroduce additional variation in the results.

The absorbance spectral data (250 nm to 600 nm=−log(l/l_(o))) is used tocalculate a corrected fluorescence spectrum to adjust for primary andsecondary inner filter effects (IFE) as represented at 348.Thefluorescence emission spectrum excited at 254 nm provides quantitativeinformation on the TOC fractions (C3, C4) associated with proteins(aromatic amino acids), but more importantly (for the representativeembodiment illustrated) the lower molecular weight and aromaticity (C2)and the higher molecular weight and aromaticity humic and fulvic acidcomponents (C1) associated with the SUVA parameter. The selected watertreatment parameters, SUVA and TOC in this example, are then calculatedor determined based on the fluorescence intensity C3, fluorescenceintensity ratio C2:C1 and the absorbance A254 as represented at 350. TheTOC can be determined based on the absorbance and fluorescence datausing the predetermined relationship between the measured TOC using aTOC meter and the predicted TOC, which may be represented as the linearcombination of the absorbance and fluorescence as illustrated anddescribed in greater detail herein, for example.

Other water treatment parameters may also be determined or calculatedusing the absorbance and fluorescence measurements. In therepresentative embodiments illustrated in FIGS. 3A and 3B, measurementsof pH, temperature (T), and chlorine dose and residual are used topredict or determine water treatment parameters associated withdisinfectant byproduct formation potential (DBFP), such astrihalomethanes (THM) and haloacetic acids (HAA), for example, asrepresented at 352. One or more water treatment parameters may becompared with an associated warning/compliance threshold at designatedor periodic time intervals as represented by 354. A calibrated ornumerical model may be used to evaluate changes in concentrations of oneor more components, such as concentrations of C3, C4 associated withprotein emission (330 and 350 nm); concentration C2 of low molecularweight humic/fulvic components (420 nm); and concentration C1 of highermolecular weight humic/fulvic components (450-475 nm). The watertreatment parameter(s) may be recorded and/or plotted for each controlpoint or treatment process as represented at 356.

FIGS. 4-8 illustrate examples of determining component concentrationsand/or ratios for use in a system or method for determining a watertreatment parameter according to embodiments of the present disclosure.The representative embodiment illustrated determines componentconcentrations for C1 and C2 as previously described. However,determination of additional component concentrations or alternativecomponent concentrations and/or ratios may be performed depending on theparticular application and implementation. Absorbance spectra inresponse to an excitation source having a nominal wavelength of 254 nmis collected from an online instrument for samples taken before andafter a representative treatment process or series of treatmentprocesses as represented at 410. The A254 value represented at 416 issubsequently used in combination with the component values or ratios(C2:C1 in this example) to indirectly determine a predicted watertreatment parameter, such as TOC, as represented at 418. In theembodiment illustrated, the absorbance spectrum is plotted as a functionof wavelength for a sample taken before a treatment process (coagulationin this example) as represented by data 412 and for a sample taken aftera treatment process as represented by data 414. As such, data 412represents the absorbance of a raw water sample and data 414 representsa water sample after treatment. The absorbance data is used for innerfilter effects (IFE) correction as represented at 420 of thefluorescence spectra data represented by the plots 430, which shownormalized intensity as a function of corrected emission wavelength inresponse to the excitation nominal wavelength of 254 nm. Depending onthe particular application and implementation, IFE may not be requiredto provide acceptable results. The fluorescence spectra represented at430 include pre-treatment (raw) model data 432, post-treatment modeldata 434, pre-treatment (raw) measured data 436 and post-treatment data438. The sample data was fit to a validated three-component (C1-C3)parallel factor analysis model with a high degree of correlation asdescribed in greater detail herein.

The fluorescence spectra data 430 can be analyzed using variousquantitative analysis strategies as generally represented by blocks 440,442, 444, and 446 and illustrated and described in greater detail withreference to FIGS. 5-8 to determine the changes in the aromaticcomponent intensities (such as C1 and C2, for example) and ratios (C1:C2or C2:C1, etc.). The component concentrations or ratios from theseanalyses as represented at 450 are combined with the A254 nm readingsrepresented at 416 to calculate the TOC using multiple linear regression(MLR) as represented at 418. As illustrated by the plot 418 with thepredicted data as a function of the measured data, the predicted ormodeled data exhibits a generally linear relationship with the measuredor directly determined data as previously described. A multiple linearregression (MLR) 460 was used to determine x and y coefficients aspreviously described with respect to FIGS. 3A and 3B to model thetreatment parameter as a linear combination of the A254 absorbance datarepresented at 416 and the fluorescence data represented by 450corresponding to the ratio or component concentrations of C1 and C2expressed as C2:C1, or alternatively C_(tot):C1 for total organicconcentration (TOC) in this example. In various embodiments thecomponent concentrations are used instead of a ratio of componentconcentrations. Similarly, calculation of a component concentration maynot be required. Rather parameters that correlate with the componentconcentrations may be used, such as peak height as described in greaterdetail herein.

As demonstrated by the strong positive correlation illustrated in data418, the absorbance and fluorescence data can be used to indirectlydetermine a water treatment parameter, such as TOC, using acorresponding model without the need for a corresponding TOC meter. Thisprovides various advantages with respect to the ability to accuratelymonitor the treatment process with an online instrument as previouslydescribed. In particular, use of the absorbance and fluorescence dataprovide a reproducible evaluation for different water sources that mayhave variable aromatic properties over time or between sources. Thekinetic simultaneity of the absorbance and fluorescence measurementsprovide a more precise determination of SUVA that eliminates propagatednoise and interferences of the separate detection methods that rely on aTOC meter as conventionally implemented. The more precise and accurateTOC and aromaticity evaluations afforded according to embodiments of thepresent disclosure facilitate compliance with more stringent regulationsfor water treatment plants.

While the representative embodiment illustrated in FIG. 4 was used todemonstrate the ability to indirectly determine TOC, other watertreatment parameters may be determined in a similar manner, such asSUVA, THMFP, etc.

As previously described, the changes in C1-C4 or other components beingmonitored in particular applications can be monitored continuously andevaluated by comparing absorbance and corrected fluorescence spectrafrom different detectors positioned to analyze samples from desiredpoints within a particular treatment process or series of processes.Various numerical operations may be employed for quantitativelyevaluating the spectral shape change associated with the change inconcentration ratios from a particular source or as a result of aparticular treatment process or series of processes.

As illustrated and described with reference to FIG. 4, the fluorescencespectra can be analyzed as represented by blocks 440, 442, 444, and 446as illustrated and described in greater detail with reference to FIGS.5-8 to determine the changes in the aromatic component intensitiesand/or associated ratios. In the representative embodiments illustrated,the component ratios from these analyses are combined with the A254 nmreadings to calculate the TOC using multiple linear regression (MLR).

FIG. 5 shows a classical least squares fit (CLS) for data associatedwith the main components C1, C2, and C3 for the raw and treated samplesto indicate the relative changes in the aromatic components C1 and C2.Intensity data is plotted as a function of wavelength from 300 nm to 600nm. Data 510 represents C1 in raw samples before a representativetreatment process, while data 512 represents effluent samples aftertreatment. Similarly, data 514 represents intensity for component C2 inraw samples while data 516 represents intensity values for component C2in effluent samples. Data 518 represents intensity values for componentC3 while data 520 represents intensity values for C3 in effluentsamples. Data 522 represents intensity values for the sum of componentsC1, C2, and C3 in raw samples, while data 524 represents intensityvalues for the component sum in effluent samples.

FIGS. 6-8 illustrate numerical approaches that will provide quantitativeinformation independent of the need to perform least squares curvefitting. FIG. 6 shows how the normalized spectra can be used tocalculate the difference in the raw and treated spectra to measure thechanges in the spectral intensities proportional to the C1 and C2concentration changes. Data 600 includes data representing pre-processor raw peak normalized absorbance corrected fluorescence spectrum data610, post-process or effluent peak normalized absorbance correctedfluorescence spectrum data 612, and the difference data 614. Asillustrated in FIG. 6, the change or shift in the spectral center ofgravity from pre-treatment to post-treatment data leads to negativevalues in the difference spectra. This results from the peak wavelengthcenter shift associated with removal of C1 components during thecoagulation treatment process. The magnitude or change in the peakdifference data is represented at 620.

FIG. 7 illustrates use of normalized integrals of raw and treated samplespectra to calculate the difference or change in area proportional tothe change in the C1 and C2 components. Data 700 includes the normalizedcumulative integral data for a pre-treatment or raw sample 710, apost-treatment or effluent sample 712, and the difference spectrum data714. The area 720 corresponds to the integral of the difference data 714over the associated wavelength range. Normalization of the cumulativeintegral of the spectrum at each treatment step as a function ofwavelength may be the most effective measure for confirming thequantitative change evaluated as the difference of the normalizedintegral spectra between steps. As illustrated in FIG. 7, the signalarea 720 of the difference spectrum remains positive and linear with thecomponent ratio change resulting from the coagulation treatment process.Similar results may be obtained for other types of treatment processesusing corresponding identification of related components and the effectof a particular treatment process or series of processes on a particularcomponent concentration or ratio of component concentrations. As alsoillustrated by the data represented in FIG. 7, and illustrated anddescribed in greater detail with reference to FIG. 8, the change orshift in wavelength coordinates or value associated with the peak of thedifference data 714 may be quantitatively related to the componentconcentration changes and may be used to monitor and/or control aparticular treatment process or series of treatment processes

FIG. 8 shows how the wavelength coordinates at −20 nm from the peak, atthe peak and +20 nm from the peak of each spectrum can be used tocalculate the changes in the C1 and C2 component intensities. Line 810represents pre-treatment or raw sample data Line 812 representspost-treatment or effluent sample data and line 814 represents thedifference data or change between lines 810 and 812. As illustrated inFIG. 8, peak 820 shifts 14 nm from pre-treatment to post treatment. Peak830 shifts about 17 nm, and peak 840 shifts about 21 nm frompre-treatment to post treatment.

As illustrated in FIGS. 4-8, the absorbance and fluorescence spectralanalyses may be used to identify the presence of unknown contaminationsas new components in the samples. As those of ordinary skill in the artwill recognize, the same type of component model can be tuned toprotein-like peaks for sewage treatment (BOD or COD), oil peaks for oilrecycling etc. While other excitation wavelengths may be used forparticular applications, the 254 nm is a very high signal peak in theexcitation spectra of all natural DOC components. It should berecognized that oxygen concentration is another factor that can bequantified in the model as an influencer of the quantum yield of thecomponents. As such, similar analyses may be used for ozone treatmentmonitoring and control.

Because the system and method rely primarily on the relative changes inthe normalized fluorescence spectral shape, area, and maxima, thecomponent concentration changes for the fluorescence spectra can also becalculated numerically based on ratiometric, subtractive, and/orderivative analyses to speed calculation rather than using a calibratedclassical least squares (CLS) approach to fit the fluorescence data tothe known component spectral shapes as previously described. Inaddition, absorbance and fluorescence ranges can be extended to includechlorophylls and accessory pigments from algae with appropriateexcitation filters and gratings in the associated online instrument(s).In addition, the relationship between protein components (C3) can alsobe used to monitor biochemical and chemical oxygen demand parameters forwaste-water treatment as previously described.

During development and model validation, samples were collected tocompare the fluorescence and absorbance spectral data of raw sourcewater from a canal and after coagulation/filtration treatment. The modelused to treat the data was first validated using complete fluorescenceexcitation-emission matrices (EEMs) corrected for fluorescence innerfilter effects and with parallel sample measurements of the totalorganic carbon measured independently with a TOC meter. The EEM data setoriginally comprised 204 samples and was fit to a validated threecomponent parallel factor analysis model with a high degree ofcorrelation (greater than 98%) and split half validated correlation(greater than 97.8%). The three components were clearly identified as alow molecular weight humic/fulvic component (C1), a high-molecularweight humic/fulvic component (C2), and protein components (C3).Analysis of samples taken after a coagulation/filtration treatmentdemonstrated that the treatment lowers the relative concentration of C2compared to C1 with little if any effect on C3.

The SUVA data calculated from the A254 nm and TOC parameters wascompared to the ratio of the C2:C1 components as a function of analysisdate for samples collected over the course of several days to monitorthe effect of sample variation. Comparison of the SUVA parameter to theC2:C1 ratio was used to demonstrate that changes in SUVA (measured usingseparate absorbance and TOC meter readings) may be more preciselymeasured using the C2:C1 ratio measured simultaneously with absorbanceand corrected fluorescence instrument according to embodiments of thepresent disclosure. The SUVA calculations showed raw water was greaterthan 4 and settled and effluent samples were generally around 2-3 withsignificant (>19 to 13%) scatter in the treated samples.

The C2:C1 ratio, which is a linear factor in relation to TOC, mirroredthe SUVA trend with a lower coefficient of variance in the treatedsamples (<5 to 3%) for better resolution of aromaticity changes amongthe three treatments. Monitoring of the A254 and TOC meter data as afunction of analysis date was performed to illustrate the consistency ofthe changes in the source water composition. The measured TOC and A254values used for the SUVA calculations were used to demonstrate thattreatment resulted in two linear relationships between the raw andtreated water consistent with the SUVA and C2:C1 ratios. Based on thisdata, it is clear the changes in the linear relationships between theraw and treated data sets as a function of TOC is consistent with thesystematic change in the molar extinction of the samples that isdetermined by the relative reduction in the C2:C1 component ratioaccording to Beer's Law.

As such, the present disclosure recognizes that knowing the C2:C1 ratio,concentrations, or other correlated values, and A254 value enables thecalculation (by the process of multiple linear regression or similarmethods) of the TOC for the data sets using one predictive equation. Themultiple linear regression equation results indicate a high degree ofprecision and accuracy of the TOC (R²>0.988). Therefore, knowing thefluorescence emission spectrum and absorbance spectrum allowssimultaneously determination of the SUVA and TOC concentrations with oneinstrument that can operate online for TOC ranges from <<0.5 to >10 mg/lwith high confidence. Online instruments according to the presentdisclosure can determine these and similar water treatment parameters orindicators within a matter of seconds for each sample using instrumentshaving few moving parts.

FIG. 9 is a detailed flow chart illustrating a system or method fordetermining a water treatment process parameter, such as dissolvedorganic carbon (DOC), total organic carbon (TOC), or trihalomethaneformation potential (THMFP), for example, using absorbance andfluorescence measurements according to embodiments of the presentdisclosure. The absorbance and fluorescence spectral blank informationis recorded for each detector in the network as represented at 910 andeach detector is synchronized for spectral calibration and response andoptical acuity (cleanliness) as represented at 912. In variousembodiments, the detector may be calibrated in situ using remote fiberaccess to the CCD spectrograph for calibration and reference materiallight sources and samples.

The sample flow can be temporarily halted with manually or automaticallycontrolled valves to route a particular sample to the flow throughcuvette as represented at 912 for subsequent absorbance and fluorescencedetermination to provide analysis synchronization as described below.The complete UV-NIR absorbance spectrum is measured from 230-800 nmusing an order sorting filter equipped (OSF) CCD-spectrograph until adesired signal to noise is obtained for each detector location asindicated at 914. The fluorescence path is shuttered to preventscattered light from entering this path to the CCD during absorbancemeasurement at 916. The absorbance path is corrected by the referencediode signal to account for light source fluctuations as represented at918.

The sample's fluorescence emission spectrum is excited at 254 nm and thesignal is integrated until the desired signal to noise is reached foreach detector location as represented at 930. The emission path isfiltered to eliminate any excitation light and theabsorbance/transmittance path is shuttered to prevent illumination ofthe CCD through this path during fluorescence measurement at 932. Thefluorescence spectrum is corrected for emission spectral response andthe reference diode at 934. The A254 nm value is evaluated from theabsorbance spectrum and if it exceeds the threshold saturation value anassociated message is generated at 936. If no saturation warning isrecorded, then the absorbance spectral data is used to apply the primaryand secondary inner filter effect corrections to the fluorescencespectral data to achieve a correct fluorescence response at 938.

The fluorescence spectra from each detector are processed by calculatinga cumulative integral curve where the total integral is normalized tounity at 940.The treated water signals from each step are subtractedfrom the source water signal to evaluate the effect of the associatedtreatment process at 942. The difference curve is evaluated in terms ofarea and wavelength peak and width to determine when the treatmentprocess, which is a coagulation process in one representativeembodiment, has had the desired effect 944. The cumulative integrals anddifference curve data are further processed with the A254 nm value usingthe previously described wavelength coordinate evaluations and equationsat 946 to provide the raw signal data parameters used tocalculate/calibrate the total dissolved organic carbon (DOC) equivalent.

As also shown in FIG. 9, the cumulative integral signals are evaluatedfor component concentration ratios at 950 using one of several methodsincluding but not limited to: classical least squares regression basedon the sum of calibrated reference spectral contributions for each C1-C4followed by calculation of the C2:C1 (or C_(tot):C1) intensity ratio at952. Residual sums can be evaluated to determine/detect and flag unknowncontaminations or calibration issues by comparing them to a giventhreshold at 954. A restrained nonlinear regression can be applied tomaintain the separate components but allow for slight deviations in peakwidths and center parameters as represented at 956. Thewavelength-intensity indices of the integral curves calibrated linearlyagainst the C2:C1 ratio, noting, normally the C3 and C4 components havea very low relative contribution to the aromatic absorbance (extinction)at 254 nm compared to C1 and C2 as represented at 958. Also C3 and C4usually have a statistically null relative response to coagulation thusexcluding them from the term tends to improve the correlation betweenthe C1 concentration and A254 nm.

The DOC is calculated using the multiple linear regression derivedparameter coefficients x and y based on the independent A254 nm andC2:C1 (or C_(tot):C1) intensity ratios, respectively as represented at960 according to: DOC=x(A254 nm)*y (C2:C1), where x and y are linearcoefficients, typically the regression is constrained to an intercept of0 and slope of unity to increase the predictive correlation capacity andsignificance. The A254 nm and C2:C1 (or C_(tot):C₁) signals would becalibrated using TOC meter data sampled in a batch mode comparison fromthe corresponding processing steps (locations) at corresponding timesusing the simple multiple linear regression (MLR) formula above todetermine x and y for predictive capacity. The term y*(C2:C1) islinearly correlated by definition to the parameter more widely known asthe specific UV absorbance coefficient (SUVA) since C2:C1 (orC_(tot):C₁) represents the change in the relative componentconcentrations for the water samples which determines the linearrelationship between A254 nm and DOC.

The linear analysis is based on recognition that the molar extinctioncoefficients for each component remain constant as a function oftreatment such that the A254 nm changes only represent changes in theabsolute and relative component concentration(s). In addition, thefluorescent quantum yields for each component also remain constant (bothabsolutely and in relation to each other) during treatment such thatchanges in the normalized fluorescence spectrum and integral shapes alsoonly indicate relative changes in the component concentrations. Minorchanges in quantum yield can be accounted for in the fluorescencespectral modeling when measured/detected as a function of therelationship between absorbance and fluorescence. Such changes may beassociated with pH, temperature, and dissolved oxygen concentration.

As previously described, the A254 nm and the C2:C1 or (C_(tot):C1) ratiocan also be used to accurately predict disinfection byproduct formationpotential in a multiple linear regression scheme potentially includingseveral variables measured with other corresponding meters in the plantoperation. Correlated/determinant parameters can include, but would notbe limited to, chlorine dose and residual, contact times, temperature,pH, alkalinity and turbidity. A typical trihalomethane formationpotential prediction (THMFP) equations is defined as:

THMFP (ppb)=a(A254 nm)+b(C2:C1)+c(pH)+d([C1−])+e(T)+f([Alk])

where a, b, c, d, and e are linear coefficients. Typically, as with theTOC prediction, an intercept of 0 and slope of unity can be constrainedin the linear equation fit parameters to increase the predictivecorrelation.

As those of ordinary skill in the art will appreciate, representativeembodiments previously described may use a ratio of components, such asC2:C1 or C1:C2. Alternatively, or in combination, componentconcentrations, component values, or other parameters that correlatewith the component concentrations or values may be used depending on theparticular application and implementation. For example, the peak heightassociated with a particular component can be used as previouslydescribed. Furthermore, various embodiments do not require measurementof the emission spectra or dispersing of the emitted light. Rather, adiode may be used as the detector with acceptable results for someapplications. Similarly, IFE correction may not be required in someapplications.

As such, various embodiments of a water treatment or similar processmonitor according to the present disclosure may provide savings orreduced use of associated treatment chemicals, such as coagulationchemicals and granulated activated carbon (on the order of tens ofthousands of dollars per month for a typical application). Similarly,various embodiments may provide savings or reduced use of BiologicallyActivated Carbon (bGAC) in terms of activity needed to remove DOC.Likewise, embodiments may reduce membrane fouling and associated pumpingenergy as well as reducing membrane damage and increasing membraneuseful life for Reverse Osmosis (RO), Forward Osmosis (FO), MembraneBioreactors (MBR), Microfiltration (MF) and Ultrafiltration (UF)applications. Online monitoring of water treatment parameters accordingto various embodiments may be used for optimization of ozone treatmentand reduction in taste and odor objections. Furthermore, variousembodiments may be used to improve detection of organic contaminantsincluding oil.

While exemplary embodiments are described above, it is not intended thatthese embodiments describe all possible forms of a system or method foranalyzing a sample to determine a water treatment parameter or indicatoraccording to the present disclosure. Rather, the words used in thespecification are words of description rather than limitation, and it isunderstood that various changes may be made without departing from thespirit and scope of the disclosure. As previously described, thefeatures of various representative embodiments may be combined in waysthat are not explicitly illustrated or described to form furtherembodiments. While various embodiments may have been described asproviding advantages or being preferred over other embodiments withrespect to one or more desired characteristics, as one of ordinary skillin the art is aware, one or more characteristics may be compromised toachieve desired system attributes, which depend on the specificapplication and implementation. These attributes include, but are notlimited to: cost, strength, durability, life cycle cost, marketability,appearance, packaging, size, serviceability, weight, manufacturability,ease of assembly, operation, etc. Any embodiments described herein asless desirable than other embodiments or prior art implementations withrespect to one or more characteristics are not outside the scope of thedisclosure and may be desirable for particular applications.

What is claimed is:
 1. A computer-implemented method for determining aparameter, comprising: receiving, by a computer, at least twomeasurements of fluorescence emission spectra of a sample including afirst peak emission wavelength and at least a second peak emissionwavelength, respectively, emitted in response to an excitationwavelength; determining, using the computer, a first component valueassociated with the first peak emission wavelength and a secondcomponent value associated with the second peak emission wavelength; andcalculating, using the computer, a value for the parameter based on atleast one coefficient calibrated with respect to a relationship betweenthe fluorescence emission spectra of the first and second components andthe parameter.
 2. The method of claim 1 wherein calculating comprisesdetermining the value for the parameter based on a linear combination ofthe first and second component values.
 3. The method of claim 1 whereincalculating comprises determining the value for the parameter based on aratio of the first and second component values.
 4. The method of claim 1wherein the first and second component values comprise first and secondcomponent concentrations.
 5. The method of claim 1 further comprising:receiving, by the computer, absorbance measurements obtained at theexcitation wavelength of the sample; correcting the fluorescenceemission spectra based on the absorbance measurements; and determiningthe value for the parameter based on a combination of the first andsecond component values, the at least one coefficient, and theabsorbance measurements.
 6. The method of claim 1 wherein therelationship comprises intensity of the fluorescence emission spectra atthe first and second peak wavelengths.
 7. The method of claim 1 whereinthe sample is a water sample and the parameter is a water treatmentparameter, the method further comprising: receiving, by the computer, aDOC value determined using a DOC meter; receiving, by the computer,absorbance measurements of the water sample; determining, by thecomputer, A254 values and SUVA values based on the absorbancemeasurements; receiving, by the computer, fluorescence emission spectrafor each DOC and A254 value; determining first and second linearcoefficients for each of a plurality of component values, including thefirst and second component values; and determining the water treatmentparameter value based on a linear combination of a ratio of the firstand second component values multiplied by the first linear coefficient,and the A254 value multiplied by the second linear coefficient.
 8. Themethod of claim 1 wherein the water treatment parameter is atrihalomethane formation potential (THMFP), the method furthercomprising: receiving a pH measurement of the water sample; receiving atemperature measurement of the water sample; receiving an alkalinitymeasurement of the water sample; and wherein calculating furtherincludes the linear combination of the pH, temperature, and alkalinitywith the ratio and the absorbance measurement.
 9. The method of claim 1wherein the measurements of fluorescence emission spectra includeintegration of values over a predetermined range of emission wavelengthsrepresenting concentration of component species associated with eachpeak emission wavelength.
 10. The method of claim 1 wherein theabsorbance and fluorescence measurements are obtained from a commoninstrument.
 11. The method of claim 1 wherein the absorbance andfluorescence measurements are obtained substantially simultaneously. 12.A system for monitoring a water treatment process including acoagulation-settling process, comprising: a first instrument positionedfor online sampling of an inlet to the coagulation-settling process, thefirst instrument measuring a first fluorescence emission spectrum of aninlet sample in response to a first excitation wavelength; a secondinstrument positioned for online sampling of an outlet from thecoagulation-settling process, the second instrument measuring a secondfluorescence emission spectrum of an outlet sample in response to thefirst excitation wavelength; and a computer in communication with thefirst and second instruments and configured to compare the firstfluorescence emission spectrum and the second fluorescence emissionspectrum for controlling the coagulation-settling process.
 13. Acomputer-implemented method for controlling a water treatment process,comprising: measuring a first fluorescence emission spectrum of apre-process water sample over a predetermined wavelength range producedin response to an excitation wavelength; normalizing the firstfluorescence emission spectrum to a predetermined peak value; measuringa second fluorescence emission spectrum of a post-process water sampleover the predetermined wavelength range produced in response to theexcitation wavelength; normalizing the second fluorescence emissionspectrum to the predetermined peak value; comparing the first and secondpeak normalized fluorescence emission spectra to determine a change indissolved organic carbon (DOC); and controlling the water treatmentprocess based on the change in DOC.
 14. The method of claim 13 whereinthe excitation wavelength is about 254 nm.
 15. The method of claim 14wherein the predetermined wavelength range is from about 290 nm to about600 nm.
 16. The method of claim 13 wherein the water treatment processcomprises a coagulation process.
 17. The method of claim 13 whereincomparing comprises calculating a difference between the first andsecond peak normalized emission spectra.
 18. The method of claim 13further comprising: integrating the first fluorescence emission spectrumover the predetermined wavelength range prior to normalizing the firstfluorescence emission spectrum; integrating the second fluorescenceemission spectrum over the predetermined wavelength range prior tonormalizing the second fluorescence emission spectrum; and whereincomparing includes comparing the normalized integrals of the first andsecond fluorescence emission spectra.
 19. The method of claim 18 whereincomparing includes determining a difference between the normalizedintegrals of the first and second fluorescence emission spectra.
 20. Themethod of claim 18 wherein comparing includes determining a shift inwavelength of a peak value for the normalized integrals of the first andsecond fluorescence emission spectra.